Flow Convergence Area Estimation on In Vitro Color Flow Doppler Images Using Deep Learning. Cheimariotis, G., Haris, K., Lee, J., White, B. E., Katsaggelos, A. K., Thomas, J. D., & Maglaveras, N. In IFMBE Proceedings, volume 76, pages 285–291. 2020. ![Flow Convergence Area Estimation on In Vitro Color Flow Doppler Images Using Deep Learning [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex We present an automatic method to estimate flow rate through the orifice in in-vitro 2D color-flow Doppler echocardiographic images. Flow rate properties are important for the assessment of pathologies like mitral regurgitation. We expect this method to be transferable to in-vivo patient data. The method consists of two main parts: (a) detecting a bounding box which encloses aliasing contours and its surroundings (namely a region representative of flow convergence area), (b) application of Convolutional Neural Networks for regression to estimate the flow convergence area. Best result achieved is the 5% mean error for validation data which is from other experiments that were used for training. Given the small number of training data, this method shows promising results.
@incollection{Brent2019,
abstract = {We present an automatic method to estimate flow rate through the orifice in in-vitro 2D color-flow Doppler echocardiographic images. Flow rate properties are important for the assessment of pathologies like mitral regurgitation. We expect this method to be transferable to in-vivo patient data. The method consists of two main parts: (a) detecting a bounding box which encloses aliasing contours and its surroundings (namely a region representative of flow convergence area), (b) application of Convolutional Neural Networks for regression to estimate the flow convergence area. Best result achieved is the 5% mean error for validation data which is from other experiments that were used for training. Given the small number of training data, this method shows promising results.},
author = {Cheimariotis, Grigorios-Aris and Haris, Kostas and Lee, Jeesoo and White, Brent E. and Katsaggelos, Aggelos K. and Thomas, James D. and Maglaveras, Nikolaos},
booktitle = {IFMBE Proceedings},
doi = {10.1007/978-3-030-31635-8_34},
isbn = {9783030316341},
issn = {14339277},
keywords = {Color flow doppler,Deep learning,Flow rate,Mitral regurgitation},
pages = {285--291},
title = {{Flow Convergence Area Estimation on In Vitro Color Flow Doppler Images Using Deep Learning}},
url = {http://link.springer.com/10.1007/978-3-030-31635-8_34},
volume = {76},
year = {2020}
}
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